# search.py
# ---------
# Licensing Information:  You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
# 
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel (pabbeel@cs.berkeley.edu).


"""
In search.py, you will implement generic search algorithms which are called by
Pacman agents (in searchAgents.py).
"""

import util

class SearchProblem:
    """
    This class outlines the structure of a search problem, but doesn't implement
    any of the methods (in object-oriented terminology: an abstract class).

    You do not need to change anything in this class, ever.
    """

    def getStartState(self):
        """
        Returns the start state for the search problem.
        """
        util.raiseNotDefined()

    def isGoalState(self, state):
        """
          state: Search state

        Returns True if and only if the state is a valid goal state.
        """
        util.raiseNotDefined()

    def getSuccessors(self, state):
        """
          state: Search state

        For a given state, this should return a list of triples, (successor,
        action, stepCost), where 'successor' is a successor to the current
        state, 'action' is the action required to get there, and 'stepCost' is
        the incremental cost of expanding to that successor.
        """
        util.raiseNotDefined()

    def getCostOfActions(self, actions):
        """
         actions: A list of actions to take

        This method returns the total cost of a particular sequence of actions.
        The sequence must be composed of legal moves.
        """
        util.raiseNotDefined()


def tinyMazeSearch(problem):
    """
    Returns a sequence of moves that solves tinyMaze.  For any other maze, the
    sequence of moves will be incorrect, so only use this for tinyMaze.
    """
    from game import Directions
    s = Directions.SOUTH
    w = Directions.WEST
    return  [s, s, w, s, w, w, s, w]

class Node_():
    def __init__(self, state, father, action, cost=0, priority=0):
        self.state = state
        self.father = father
        self.action = action
        self.cost = cost
        self.priority = priority




def depthFirstSearch(problem: SearchProblem):
    """
    Search the deepest nodes in the search tree first.

    Your search algorithm needs to return a list of actions that reaches the
    goal. Make sure to implement a graph search algorithm.

    To get started, you might want to try some of these simple commands to
    understand the search problem that is being passed in:

    print("Start:", problem.getStartState())
    print("Is the start a goal?", problem.isGoalState(problem.getStartState()))
    print("Start's successors:", problem.getSuccessors(problem.getStartState()))
    
    getStart -> state (a tuple(x,y))
    getSuccessors -> [ (successor, action, stepCost) ]
    
    """
    "*** YOUR CODE HERE ***"
    st = problem.getStartState()
    if st is None: return []
    move = util.Stack()
    visit = set()
    
    def dfs(state)-> bool:
        nonlocal move
        nonlocal visit
        if(problem.isGoalState(state)): return True
        # else
        successors = problem.getSuccessors(state)
        if successors is None: return False
        for successor in successors:
            if successor[0] in visit: continue
            # else
            visit.add(successor[0])
            move.push(successor[1])
            if(dfs(successor[0])): return True
            move.pop()
        return False
    
    visit.add(st)
    if dfs(st): return move.list
    # else
    util.raiseNotDefined()


def breadthFirstSearch(problem: SearchProblem):
    """Search the shallowest nodes in the search tree first."""
    "*** YOUR CODE HERE ***"
    visted = set()
    edges = util.Queue()
    
    edges.push(Node_(problem.getStartState(),None,None))
    
    while not edges.isEmpty():
        node = edges.pop()
        if problem.isGoalState(node.state):
            move = util.Queue()
            while node.action is not None:
                move.push(node.action)
                node = node.father
            return move.list
        # else
        if node.state not in visted:
            visted.add(node.state)
            for successor in problem.getSuccessors(node.state):
                # state, action, cost 
                edges.push(Node_(successor[0],node,successor[1]))
    return []
    util.raiseNotDefined()

def uniformCostSearch(problem: SearchProblem):
    """Search the node of least total cost first."""
    "*** YOUR CODE HERE ***"
    visted = set()
    edges = util.PriorityQueueWithFunction(lambda node:node.cost)
    
    edges.push(Node_(problem.getStartState(),None,None,0))
    
    while not edges.isEmpty():
        node = edges.pop()
        if problem.isGoalState(node.state):
            move = util.Queue()
            while node.action is not None:
                move.push(node.action)
                node = node.father
            return move.list
        # else
        if node.state not in visted:
            visted.add(node.state)
            for successor in problem.getSuccessors(node.state):
                # state, action, cost 
                edges.push(Node_(successor[0],node,successor[1],successor[2]+node.cost))
    return []

def nullHeuristic(state, problem=None):
    """
    A heuristic function estimates the cost from the current state to the nearest
    goal in the provided SearchProblem.  This heuristic is trivial.
    """
    return 0

def aStarSearch(problem: SearchProblem, heuristic=nullHeuristic):
    """Search the node that has the lowest combined cost and heuristic first."""
    "*** YOUR CODE HERE ***"
    visted = set()
    edges = util.PriorityQueue()
    
    edges.push(Node_(problem.getStartState(),None,None,0),heuristic(problem.getStartState(),problem))
    
    while not edges.isEmpty():
        node = edges.pop()
        if problem.isGoalState(node.state):
            move = util.Queue()
            while node.action is not None:
                move.push(node.action)
                node = node.father
            return move.list
        # else
        if node.state not in visted:
            visted.add(node.state)
            for successor in problem.getSuccessors(node.state):
                this_state, this_action, this_cost = successor
                if this_state in visted: continue
                total_cost = successor[2]+node.cost
                edges.push(Node_(this_state, node, this_action, total_cost), total_cost+heuristic(this_state,problem))
    return []


# Abbreviations
bfs = breadthFirstSearch
dfs = depthFirstSearch
astar = aStarSearch
ucs = uniformCostSearch
